Algunas Implementaciones de Gradient-Boosting.

Universidad Central

Maestría en analítica de datos

Métodos estadísticos para analítica de datos.

Docente: Luis Andrés Campos Maldonado.

# Librerias para Colab
# !pip install xgboost catboost lightgbm sweetviz

# Paquetes a usar.
import warnings
warnings.filterwarnings("ignore")

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
from sweetviz import analyze
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import RandomizedSearchCV, train_test_split, cross_validate
from sklearn.pipeline import Pipeline
from xgboost.sklearn import XGBRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor

plt.rcParams["figure.figsize"] = (15,6)
pd.set_option("display.max_columns", None)

url_base = "https://raw.githubusercontent.com/lacamposm/Metodos-Estadisticos/main/data/"

Introducción a XGBoost, LightGBM y CatBoost

Los modelos de Gradient Boosting son algoritmos de aprendizaje supervisado que combinan múltiples modelos base (habitualmente árboles de decisión) para construir un modelo predictivo más fuerte. Este enfoque se basa en entrenar modelos de manera secuencial, donde cada modelo corrige los errores cometidos por los modelos anteriores.

A lo largo de los años, han surgido implementaciones optimizadas de Gradient Boosting que mejoran la velocidad, la precisión y la facilidad de uso. Entre las más populares se encuentran:

• XGBoost (Extreme Gradient Boosting): Reconocido por su alto rendimiento y flexibilidad.

• LightGBM (Light Gradient Boosting Machine): Diseñado para ser rápido y eficiente en el uso de memoria.

• CatBoost (Categorical Boosting): Especializado en el manejo de datos categóricos.

Regularización en XGBoost, LightGBM y CatBoost

La regularización es una técnica fundamental en aprendizaje automático para prevenir el sobreajuste (overfitting), que ocurre cuando un modelo se ajusta demasiado a los datos de entrenamiento y pierde la capacidad de generalizar sobre nuevos datos. Los algoritmos de Gradient Boosting, como XGBoost, LightGBM y CatBoost, integran diversas formas de regularización para equilibrar la complejidad del modelo y su capacidad predictiva.

¿Qué regula la regularización?

En Gradient Boosting, la regularización actúa directamente sobre los árboles base y sus contribuciones al modelo final. Específicamente:

1. Los pesos de las hojas de los árboles (\(w_j\)):

   • Estos pesos determinan el valor predicho por cada hoja.

   • Penalizaciones como \(L_{1}\) y \(L_{2}\) ajustan estos pesos para reducir su magnitud o eliminarlos por completo, evitando ajustes excesivos.

2. La estructura de los árboles (profundidad y número de hojas):

   • Al limitar la expansión de los árboles, la regularización previene que los árboles aprendan patrones específicos de los datos de entrenamiento que no generalizan bien.

3. El impacto de cada árbol base \(f_m(x)\) en el ensemble final:

   • A través del coeficiente de aprendizaje (\(\nu\)), se escala la contribución de cada árbol, asegurando actualizaciones más progresivas y menos propensas al sobreajuste.

Conceptos clave de regularización

En el contexto del boosting, la regularización se introduce para controlar la complejidad de los árboles y el impacto de cada modelo base en el ensemble final. Las formas más comunes de regularización incluyen:

• Penalización L1 (Lasso):
  Introduce un término de penalización proporcional a la suma de los valores absolutos de los pesos de las hojas:

  \[ \Omega_L = \alpha \sum_{j=1}^T |w_j|\]

  Esto fomenta que algunos pesos sean exactamente 0, eliminando hojas innecesarias y promoviendo modelos más simples.

• Penalización L2 (Ridge):
  Agrega un término de penalización proporcional al cuadrado de los pesos de las hojas:

  \[\Omega_R = \frac{\lambda}{2} \sum_{j=1}^T w_j^2\]

  Esto reduce la magnitud de los pesos, suavizando las predicciones y haciendo el modelo más conservador.

XGBoost (Extreme Gradient Boosting)

XGBoost es una implementación avanzada de Gradient Boosting, conocida por su alto rendimiento, precisión y flexibilidad. Es ampliamente utilizada en competiciones de ciencia de datos y problemas del mundo real debido a su capacidad para manejar datos grandes y complejos.

Características principales:

1. Regularización avanzada:

   • XGBoost utiliza penalizaciones para controlar la complejidad de los árboles y evitar el sobreajuste, promoviendo modelos más simples y generalizables.

2. Optimización eficiente:

   • Implementa cálculos paralelos, lo que acelera el entrenamiento en comparación con otras herramientas.

3. Control de la estructura de los árboles:

   • Limita el crecimiento de los árboles, asegurando que no sean excesivamente complejos y puedan generalizar mejor.

Ventajas:

• Precisión: Ofrece resultados muy exactos gracias a sus técnicas avanzadas de regularización.

• Escalabilidad: Es capaz de manejar grandes conjuntos de datos de manera eficiente.

• Flexibilidad: Permite personalizar la función de pérdida y trabajar con diferentes tipos de datos.

Limitaciones:

• Puede ser más lento que otras herramientas en conjuntos de datos extremadamente grandes.

• Requiere un ajuste cuidadoso de hiperparámetros para obtener su máximo potencial.

Cuándo usar XGBoost:

• Para problemas que requieren alta precisión.

• En tareas donde el control y ajuste de los parámetros son cruciales.

• En competiciones de ciencia de datos o proyectos complejos.

Hiperparámetros de XGBoost y consejos de su uso

Configurar los hiperparámetros de XGBoost de manera adecuada es crucial para obtener un modelo óptimo. Aunque el ajuste depende del problema específico, existen valores iniciales recomendados y estrategias comunes para lograr un buen desempeño.

Hiperparámetros principales

1. learning_rate (Coeficiente de aprendizaje):

   • Controla el impacto de cada árbol base en el modelo final.

   • Recomendación: Usar un valor pequeño como 0.01 o 0.1 y aumentar el número de árboles (n_estimators).

   • Consejo: Si se requiere mayor velocidad de entrenamiento, valores más altos como 0.2 o 0.3 pueden ser utilizados, pero existe mayor riesgo de sobreajuste.

2. max_depth (Profundidad máxima):

   • Determina la profundidad máxima de los árboles base, controlando la complejidad del modelo.

   • Recomendación:

     • Usar valores pequeños (3 a 6) para conjuntos de datos pequeños o con ruido.

     • Valores mayores (6 a 10) pueden funcionar mejor para datos grandes, aunque aumentan el riesgo de sobreajuste.

3. n_estimators (Número de árboles):

   • Define cuántos árboles base se ajustarán secuencialmente.

   • Recomendación:

     • Comenzar con un valor alto (100 a 500) si se usa un learning_rate bajo.

     • Utilizar validación cruzada para determinar el número óptimo.

4. subsample (Tasa de muestreo):

   • Especifica la fracción de datos utilizados para construir cada árbol.

   • Recomendación:

     • Valores entre 0.7 y 0.9 suelen funcionar bien.

     • Reducir este valor ayuda a prevenir el sobreajuste.

5. colsample_bytree (Columnas por árbol):

   • Fracción de características consideradas al construir cada árbol.

   • Recomendación:

     • Valores entre 0.5 y 0.8 equilibran precisión y tiempo de entrenamiento.

6. reg_alpha (Regularización L1):

   • Controla la penalización L1, promoviendo modelos más esparsos.

   • Recomendación:

     • Comenzar con valores bajos (0 a 1) y aumentar si hay sobreajuste.

7. reg_lambda (Regularización L2):

   • Controla la penalización L2, reduciendo la magnitud de los pesos de las hojas.

   • Recomendación:

     • Valores moderados (1 a 10) suelen ser efectivos.

8. gamma (Penalización por división):

   • Requiere que las divisiones superen un umbral mínimo de ganancia.

   • Recomendación:

     • Valores iniciales de 0 (sin penalización) y aumentar si se detecta sobreajuste.

Estrategia para el ajuste de hiperparámetros

1. Comenzar con valores predeterminados:

   • XGBoost tiene configuraciones predeterminadas robustas para muchos problemas.

   • Ajustar primero learning_rate, max_depth, y n_estimators.

2. Validación cruzada:

   • Utilizar validación cruzada para evaluar el rendimiento en diferentes configuraciones de hiperparámetros.

3. Grid Search o Random Search:

   • Implementar búsquedas sistemáticas (GridSearchCV) o aleatorias (RandomizedSearchCV) para explorar combinaciones de hiperparámetros.

4. Experimentar con datos grandes:

   • Reducir subsample y colsample_bytree para acelerar el entrenamiento y evitar sobreajuste.

Ejemplo 1.

# Importamos el DataSet.
df = pd.read_csv(url_base + "kc_house_data.csv",)
df.head()

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	condition	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
0	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	0	3	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650
1	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	0	3	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
2	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	0	3	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
3	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	0	5	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
4	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	0	3	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503

df_temp = df.copy()
df_temp = df_temp.drop(columns=["id", "sqft_living15", "sqft_lot15", "date"])
df_temp["renovated"] = df_temp["yr_renovated"] > 0
df_temp.drop(columns=["yr_renovated"], inplace=True)
df_temp[["waterfront", "view", "renovated", "zipcode"]] = df_temp[["waterfront", "view", "renovated", "zipcode"]].astype("category")

report = analyze(df_temp, target_feat="price")
report.show_notebook()
del df_temp

plt.style.use("ggplot")

# Prepocesamiento de la Data.
df_to_model = df.copy()
df_to_model = df_to_model.drop(columns=["id", "sqft_living15", "sqft_lot15", "date"])
df_to_model["renovated"] = df_to_model["yr_renovated"] > 0
df_to_model.drop(columns=["yr_renovated"], inplace=True)
df_to_model.drop_duplicates(inplace=True)

# Definimos variable objetivo y predictores. Tomamos además set de train y set de test.
X = df_to_model.drop(columns=["price"])
y = df_to_model["price"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

features_categoric = ["waterfront", "view", "renovated", "zipcode"]
features_numerical = [col for col in X.columns if col not in features_categoric]

preprocessor = ColumnTransformer(
    transformers=[
        ("cat", OneHotEncoder(), features_categoric),
        ("num", "passthrough", features_numerical)
    ]
)

Modelo con XGBoost

xgb_regressor_ = XGBRegressor()
xgb_regressor_.fit(X_train, y_train)

print(f"Train set score: {xgb_regressor_.score(X_train, y_train):.4f}")
print(f"Test set score:  {xgb_regressor_.score(X_test, y_test):.4f}")

Train set score: 0.9758
Test set score:  0.8693

xgb_regressor1 = XGBRegressor(
    objective="reg:squarederror",
    max_depth=3,
    n_estimators=2000,
    learning_rate=0.0075,
    reg_alpha=300,
    reg_lambda=250,
    max_leaves=150,
    subsample=0.8,
)

pipeline = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", xgb_regressor1)
])

pipeline.fit(X_train, y_train)

print(f"R2 en Train: {pipeline.score(X_train, y_train):.4f}")
print(f"R2 en Test:  {pipeline.score(X_test, y_test):.4f}")

cv_xgb = cross_validate(
    pipeline,
    X_train,
    y_train,
    cv=5,
    scoring="r2",
    return_train_score=True
)

train_lr, test_lr = cv_xgb["train_score"], cv_xgb["test_score"]

plt.plot(cv_xgb["train_score"], label=f"Train. R2 in Train: {train_lr.mean():.4f}")
plt.plot(cv_xgb["test_score"], label=f"Test.  R2 in Test:  {test_lr.mean():.4f}")
plt.title("XGBoost - 5folds CV")
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.legend()
plt.show()

R2 en Train: 0.8524
R2 en Test:  0.8425

cv_xgb = cross_validate(
    pipeline,
    X_train,
    y_train,
    cv=5,
    scoring="neg_mean_absolute_error",
    return_train_score=True
)

train_lr, test_lr = cv_xgb["train_score"], cv_xgb["test_score"]


plt.plot(cv_xgb["train_score"], label=f"Train. MAE in Train: {train_lr.mean():.4f}")
plt.plot(cv_xgb["test_score"], label=f"Test.  MAE in Test:  {test_lr.mean():.4f}")
plt.title("XGBoost - 5fold CV")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

df_results_train = pd.DataFrame(
    {
        "true_value": y_train,
        "predicted_value": pipeline.predict(X_train)
    }
)

df_results_train["diff"] = abs(df_results_train["true_value"] - df_results_train["predicted_value"])
display(df_results_train.sort_values("diff", ascending=False).head(20))

df_results_test = pd.DataFrame(
    {
        "true_value": y_test,
        "predicted_value": pipeline.predict(X_test)
    }
)

df_results_test["diff"] = abs(df_results_test["true_value"] - df_results_test["predicted_value"])
df_results_test.sort_values("diff", ascending=False).head(20)

	true_value	predicted_value	diff
7252	7700000.0	2900098.750	4799901.250
3914	7062500.0	3157875.000	3904625.000
9254	6885000.0	3147814.250	3737185.750
4411	5570000.0	3001758.750	2568241.250
1448	5350000.0	3159702.000	2190298.000
1315	5300000.0	3352317.500	1947682.500
1164	5110800.0	3348500.750	1762299.250
8638	4489000.0	2940750.000	1548250.000
8092	4668000.0	3306771.500	1361228.500
2626	4500000.0	3201104.250	1298895.750
9487	2375000.0	1127361.250	1247638.750
14139	2110000.0	906920.500	1203079.500
656	3070000.0	1873606.000	1196394.000
21372	490000.0	1649058.625	1159058.625
21576	3567000.0	2420243.250	1146756.750
19484	2395000.0	1282102.625	1112897.375
5449	2850000.0	1774158.625	1075841.375
7432	2575000.0	1506080.000	1068920.000
18482	3650000.0	2591424.500	1058575.500
19684	2205000.0	1175083.375	1029916.625

	true_value	predicted_value	diff
12370	4208000.0	2.548266e+06	1.659734e+06
10446	3400000.0	1.952878e+06	1.447122e+06
7311	2500000.0	1.194972e+06	1.305028e+06
21050	900000.0	2.162562e+06	1.262562e+06
5617	2400000.0	1.201847e+06	1.198153e+06
19017	3800000.0	2.633888e+06	1.166112e+06
15258	3065000.0	1.920782e+06	1.144218e+06
13256	2546000.0	1.486281e+06	1.059719e+06
16302	3710000.0	2.653926e+06	1.056074e+06
10373	2983000.0	1.972321e+06	1.010679e+06
11535	3200000.0	2.200603e+06	9.993970e+05
19858	2700000.0	1.731232e+06	9.687684e+05
13020	2300000.0	1.371136e+06	9.288639e+05
5880	3418800.0	2.559992e+06	8.588085e+05
20295	3204000.0	2.347220e+06	8.567798e+05
10539	1700000.0	8.463644e+05	8.536356e+05
17558	2510000.0	1.658604e+06	8.513959e+05
15668	1680000.0	2.502938e+06	8.229380e+05
10468	2000000.0	1.182735e+06	8.172651e+05
11278	2100000.0	1.284781e+06	8.152186e+05

Busqueda de Hiperparametros xgb

xgb_regressor = XGBRegressor(objective="reg:squarederror")

pipeline_xgb = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", xgb_regressor)
])

param_distributions = {
    "model__max_depth": [3, 4, 5, 6],
    "model__n_estimators": [100, 500, 1000],
    "model__learning_rate": [0.0075, 0.01, 0.05],
    "model__reg_alpha": [0, 5, 10, 15, 20],
    "model__reg_lambda": [1, 10, 20, 30],
    "model__subsample": [0.6, 0.8, 1.0],
    "model__max_leaves": [100, 200, 300, 500]
}

random_search_xgb = RandomizedSearchCV(
    estimator=pipeline_xgb,
    param_distributions=param_distributions,
    n_iter=10,
    scoring="r2",
    cv=5,
    random_state=42,
    n_jobs=-1,
    return_train_score=True
)

random_search_xgb.fit(X_train, y_train)

print("Best parameters found: ", random_search_xgb.best_params_)
print(f"Best CV score: {random_search_xgb.best_score_}")

best_model = random_search_xgb.best_estimator_
print(f"R2 en Train: {best_model.score(X_train, y_train):.4f}")
print(f"R2 en Test:  {best_model.score(X_test, y_test):.4f}")

pd.DataFrame(random_search_xgb.cv_results_).to_parquet("data/xgb_cv_search.parquet")

Best parameters found:  {'model__subsample': 0.8, 'model__reg_lambda': 1, 'model__reg_alpha': 10, 'model__n_estimators': 1000, 'model__max_leaves': 300, 'model__max_depth': 3, 'model__learning_rate': 0.05}
Best CV score: 0.8905389750992884
R2 en Train: 0.9461
R2 en Test:  0.8828

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
Cell In[12], line 38
     35 print(f"R2 en Train: {best_model.score(X_train, y_train):.4f}")
     36 print(f"R2 en Test:  {best_model.score(X_test, y_test):.4f}")
---> 38 pd.DataFrame(random_search_xgb.cv_results_).to_parquet("data/xgb_cv_search.parquet")

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/util/_decorators.py:333, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    327 if len(args) > num_allow_args:
    328     warnings.warn(
    329         msg.format(arguments=_format_argument_list(allow_args)),
    330         FutureWarning,
    331         stacklevel=find_stack_level(),
    332     )
--> 333 return func(*args, **kwargs)

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/core/frame.py:3113, in DataFrame.to_parquet(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)
   3032 """
   3033 Write a DataFrame to the binary parquet format.
   3034 
   (...)
   3109 >>> content = f.read()
   3110 """
   3111 from pandas.io.parquet import to_parquet
-> 3113 return to_parquet(
   3114     self,
   3115     path,
   3116     engine,
   3117     compression=compression,
   3118     index=index,
   3119     partition_cols=partition_cols,
   3120     storage_options=storage_options,
   3121     **kwargs,
   3122 )

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/io/parquet.py:480, in to_parquet(df, path, engine, compression, index, storage_options, partition_cols, filesystem, **kwargs)
    476 impl = get_engine(engine)
    478 path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
--> 480 impl.write(
    481     df,
    482     path_or_buf,
    483     compression=compression,
    484     index=index,
    485     partition_cols=partition_cols,
    486     storage_options=storage_options,
    487     filesystem=filesystem,
    488     **kwargs,
    489 )
    491 if path is None:
    492     assert isinstance(path_or_buf, io.BytesIO)

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/io/parquet.py:198, in PyArrowImpl.write(self, df, path, compression, index, storage_options, partition_cols, filesystem, **kwargs)
    195     merged_metadata = {**existing_metadata, **df_metadata}
    196     table = table.replace_schema_metadata(merged_metadata)
--> 198 path_or_handle, handles, filesystem = _get_path_or_handle(
    199     path,
    200     filesystem,
    201     storage_options=storage_options,
    202     mode="wb",
    203     is_dir=partition_cols is not None,
    204 )
    205 if (
    206     isinstance(path_or_handle, io.BufferedWriter)
    207     and hasattr(path_or_handle, "name")
    208     and isinstance(path_or_handle.name, (str, bytes))
    209 ):
    210     if isinstance(path_or_handle.name, bytes):

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/io/parquet.py:140, in _get_path_or_handle(path, fs, storage_options, mode, is_dir)
    130 handles = None
    131 if (
    132     not fs
    133     and not is_dir
   (...)
    138     # fsspec resources can also point to directories
    139     # this branch is used for example when reading from non-fsspec URLs
--> 140     handles = get_handle(
    141         path_or_handle, mode, is_text=False, storage_options=storage_options
    142     )
    143     fs = None
    144     path_or_handle = handles.handle

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/io/common.py:749, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    747 # Only for write methods
    748 if "r" not in mode and is_path:
--> 749     check_parent_directory(str(handle))
    751 if compression:
    752     if compression != "zstd":
    753         # compression libraries do not like an explicit text-mode

File ~/github_public/Metodos-Estadisticos/venv/lib/python3.10/site-packages/pandas/io/common.py:616, in check_parent_directory(path)
    614 parent = Path(path).parent
    615 if not parent.is_dir():
--> 616     raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'")

OSError: Cannot save file into a non-existent directory: 'data'

LightGBM (Light Gradient Boosting Machine)

LightGBM es una implementación de Gradient Boosting diseñada para ser rápida, eficiente en el uso de memoria y capaz de manejar grandes volúmenes de datos con alta dimensionalidad. Es particularmente adecuada para problemas donde la velocidad y la escalabilidad son críticas.

Características principales

1. Crecimiento de árboles basado en hojas (leaf-wise):

   • A diferencia de XGBoost, LightGBM divide las hojas con mayor ganancia en cada iteración, lo que puede generar árboles más profundos y precisos.

   • Ventaja: Mejora la eficiencia del modelo, especialmente en datos grandes.

   • Desafío: Mayor riesgo de sobreajuste en conjuntos de datos pequeños.

2. Divisiones basadas en histogramas:

   • Agrupa los valores continuos en histogramas, reduciendo significativamente el tiempo de cómputo.

3. Soporte nativo para variables categóricas:

   • Permite manejar variables categóricas directamente, sin necesidad de codificación adicional.

4. Uso eficiente de memoria:

   • Consume menos memoria que XGBoost gracias a su diseño basado en histogramas.

Hiperparámetros principales y su uso recomendado

1. learning_rate (Coeficiente de aprendizaje):

   • Controla la magnitud de las actualizaciones en cada iteración.

   • Recomendación: Usar valores bajos (0.01 a 0.1) y compensar con un número alto de árboles (num_iterations).

2. max_depth (Profundidad máxima):

   • Limita la profundidad de los árboles para evitar sobreajuste.

   • Recomendación: Usar -1 para desactivar la limitación, o valores bajos (5 a 10) para datos con ruido.

3. num_leaves (Número máximo de hojas):

   • Controla la cantidad de divisiones posibles en un árbol.

   • Recomendación:

     • Valores iniciales como \(2^{\text{max\_depth}} - 1\).

     • Reducir este valor si hay sobreajuste.

4. min_child_samples (Tamaño mínimo de muestra en hojas):

   • Número mínimo de datos requeridos para crear una hoja.

   • Impacto: Ayuda a controlar hojas pequeñas que pueden sobreajustarse a los datos de entrenamiento.

   • Recomendación:

     • Para datos grandes: 20 a 100.

     • Para datos pequeños: 5 a 20.

   • Consejo: Incrementar este valor si el modelo muestra signos de sobreajuste.

5. min_data_in_leaf (Tamaño mínimo de muestra en hojas):

   • Evita hojas muy pequeñas que pueden causar sobreajuste.

   • Recomendación:

     • Para datos grandes: 20 a 50.

     • Para datos pequeños: 10 o menos.

6. feature_fraction (Fracción de características por árbol):

   • Porcentaje de características utilizadas en cada iteración.

   • Recomendación: Valores de 0.7 a 1.0.

7. bagging_fraction (Fracción de datos por iteración):

   • Controla la proporción de datos de entrenamiento utilizados en cada árbol.

   • Recomendación:

     • 0.7 a 0.9 para reducir el riesgo de sobreajuste.

     • Usar junto con bagging_freq para habilitar el muestreo.

8. reg_alpha (Regularización L1):

   • Introduce una penalización L1 que fomenta la esparsidad en los pesos de las hojas, eliminando aquellas que son menos relevantes.

   • Impacto: Reduce el número de hojas efectivas en los árboles.

   • Recomendación:

     • Comenzar con valores bajos como 0.0 o 1.0 y ajustar según el desempeño del modelo.

9. reg_lambda (Regularización L2):

   • Controla la penalización L2 sobre los pesos de las hojas, reduciendo su magnitud.

   • Impacto: Suaviza las predicciones y previene que los pesos de las hojas crezcan demasiado.

   • Recomendación:

     • Valores iniciales entre 0.0 y 10.0.

     • Incrementar si el modelo muestra sobreajuste.

10. min_gain_to_split (Ganancia mínima para división):

• Controla las divisiones insignificantes en los árboles.

• Recomendación:

  • Valores de 0.0 (sin penalización) a 0.1 para datos más complejos.

Estrategia para el ajuste de hiperparámetros

1. Comenzar con valores predeterminados:

   • LightGBM tiene configuraciones predeterminadas que suelen ser efectivas.

   • Ajustar primero learning_rate, num_leaves, y max_depth.

2. Validación cruzada:

   • Utilizar validación cruzada para medir el rendimiento y detectar sobreajuste o subajuste.

3. Pruebas de bagging y fracciones de características:

   • Probar con bagging_fraction y feature_fraction para reducir el tiempo de entrenamiento y mejorar la generalización.

4. Exploración con Grid Search o Random Search:

   • Probar combinaciones sistemáticas o aleatorias de hiperparámetros para encontrar la mejor configuración.

Modelo LigthGBM

lgbm = LGBMRegressor(verbose=-1)

pipeline_lgbm = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", lgbm)
])

pipeline_lgbm.fit(X_train, y_train)
print(pipeline_lgbm.score(X_train, y_train))
print(pipeline_lgbm.score(X_test, y_test))

0.9392381025286636
0.8792562594391012

cv_lgbm = cross_validate(
    lgbm,
    X_train,
    y_train,
    cv=5,
    scoring="r2",
    return_train_score=True
)


plt.plot(cv_lgbm["train_score"], label=f"Train. R2 in Train: {cv_lgbm['train_score'].mean():.4f}")
plt.plot(cv_lgbm["test_score"], label=f"Test.  R2 in Test:  {cv_lgbm['test_score'].mean():.4f}")
plt.title("LigthGBM - 5fold CV")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

lgbm1 = LGBMRegressor(
    objective="regression",
    reg_alpha=25,
    min_child_samples=150,
    max_depth=4,    
    reg_lambda=30
    
)

pipeline_lgbm1 = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", lgbm1)
])

pipeline_lgbm1.fit(X_train, y_train)

print(pipeline_lgbm1.score(X_train, y_train))
print(pipeline_lgbm1.score(X_test, y_test))

0.8438249657130915
0.8468238962295872

cv_lgbm1 = cross_validate(
    pipeline_lgbm1,
    X_train,
    y_train,
    cv=5,
    scoring="r2",
    return_train_score=True
)

plt.plot(cv_lgbm1["train_score"], label=f"Train. MAE in Train: {cv_lgbm1['train_score'].mean():.4f}")
plt.plot(cv_lgbm1["test_score"], label=f"Test.  MAE in Test:  {cv_lgbm1['test_score'].mean():.4f}")
plt.title("LigthGBM - 5fold CV")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

lgbm2 = LGBMRegressor(
    objective="gamma",
    reg_alpha=2,
    min_child_samples=300,
    learning_rate=0.0075,
    reg_lambda=10,
    n_estimators=1500,
    max_depth=3,
    subsample=0.9,
    subsample_for_bin=1000,
    num_leaves=300,
    n_jobs=-1,
)

pipeline_lgbm2 = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", lgbm2)
])

pipeline_lgbm2.fit(X_train, y_train)

print(pipeline_lgbm2.score(X_train, y_train))
print(pipeline_lgbm2.score(X_test, y_test))

0.8239901867809278
0.8315345203942487

cv_lgbm2 = cross_validate(
    pipeline_lgbm2,
    X_train,
    y_train,
    cv=5,
    scoring="r2",
    return_train_score=True
)

plt.plot(cv_lgbm2["train_score"], label=f"Train. MAE in Train: {cv_lgbm2['train_score'].mean():.4f}")
plt.plot(cv_lgbm2["test_score"], label=f"Test.  MAE in Test:  {cv_lgbm2['test_score'].mean():.4f}")
plt.title("LigthGBM - 5fold CV")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

px.scatter(
    pd.DataFrame(
        {
            "true_value": y_test,
            "predicted": pipeline_lgbm2.predict(X_test)
        }
    ),
    x="true_value",
    y="predicted",
    title="Prediccion VS Valor Real"
)

lgbm3 = LGBMRegressor(
    objective="gamma",
    reg_alpha=2,
    min_child_samples=200,
    learning_rate=0.01,
    n_estimators=500,    
    max_depth=6,
    subsample=0.9
)

pipeline_lgbm3 = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", lgbm3)
])

pipeline_lgbm3.fit(X_train, y_train)

print(pipeline_lgbm3.score(X_train, y_train))
print(pipeline_lgbm3.score(X_test, y_test))

cv_lgbm3 = cross_validate(
    pipeline_lgbm3,
    X_train,
    y_train,
    cv=5,
    scoring=("r2"),
    return_train_score=True
)

plt.plot(cv_lgbm3["train_score"], label=f"Train. R2 in Train: {cv_lgbm3['train_score'].mean():.4f}")
plt.plot(cv_lgbm3["test_score"], label=f"Test.  R2 in Test:  {cv_lgbm3['test_score'].mean():.4f}")
plt.title("LigthGBM - 5fold CV")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

0.8293522503882406
0.8401752759961569

lgbm = LGBMRegressor(verbose=-1)

pipeline_lgbm = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("model", lgbm)
])

param_distributions_lgbm = {
    "model__objective": ["regression", "gamma"],
    "model__num_leaves": [20, 31, 50, 100, 200],
    "model__learning_rate": [0.005, 0.0075, 0.01, 0.02, 0.05],
    "model__n_estimators": [100, 300, 500, 1000, 2000],
    "model__max_depth": [2, 3, 4, 5, 6],
    "model__min_child_samples": [150, 155, 165, 175, 190, 200],
    "model__min_split_gain": [0.0, 0.1, 0.2, 0.5],
    "model__subsample": [0.7, 0.8, 0.9],
    "model__reg_alpha": [1, 5, 10, 20],
    "model__reg_lambda": [1, 5, 10, 20],
}

random_search_lgbm = RandomizedSearchCV(
    estimator=pipeline_lgbm,
    param_distributions=param_distributions_lgbm,
    n_iter=10,
    scoring="r2",
    cv=5,
    verbose=3,
    random_state=42,
    n_jobs=-1,
    return_train_score=True
)

random_search_lgbm.fit(X_train, y_train)

print("Best parameters found: ", random_search_lgbm.best_params_)
print(f"Best CV score: {random_search_lgbm.best_score_:.4f}")

pd.DataFrame(random_search_lgbm.cv_results_)#.to_parquet("data/lgbm_cv_search.parquet")

Fitting 5 folds for each of 10 candidates, totalling 50 fits

CatBoost (Categorical Boosting)

CatBoost es una implementación avanzada de Gradient Boosting desarrollada por Yandex, especializada en manejar variables categóricas de manera eficiente. Destaca por su facilidad de uso, configuraciones predeterminadas robustas y capacidad para evitar el sobreajuste.

Características principales

1. Soporte nativo para variables categóricas:

   • A diferencia de otras bibliotecas, CatBoost maneja variables categóricas directamente, utilizando codificaciones como mean encoding supervisado de forma segura.

2. Boosting basado en órdenes:

   • En lugar de entrenar con los datos en su orden natural, utiliza permutaciones para mejorar la estabilidad y reducir el riesgo de sobreajuste.

3. Optimización eficiente:

   • Compatible con procesamiento en GPU, lo que acelera el entrenamiento en grandes conjuntos de datos.

4. Robustez frente a valores faltantes:

   • Puede manejar datos faltantes directamente, sin necesidad de preprocesarlos.

Hiperparámetros principales y su uso recomendado

1. learning_rate (Coeficiente de aprendizaje):

   • Controla la magnitud de las actualizaciones en cada iteración.

   • Recomendación: Usar valores bajos (0.01 a 0.1) para evitar sobreajuste, especialmente en datos pequeños.

2. depth (Profundidad máxima):

   • Define la profundidad máxima de los árboles.

   • Recomendación:

     • Valores entre 6 y 10 funcionan bien para la mayoría de los casos.

     • Reducir este valor para datos pequeños o con ruido.

3. iterations (Número de iteraciones o árboles):

   • Determina cuántos árboles se entrenan secuencialmente.

   • Recomendación:

     • Usar valores entre 500 y 1000 para modelos precisos.

     • Ajustar según el learning_rate: más iteraciones para valores bajos.

4. l2_leaf_reg (Regularización L2):

   • Controla la penalización L2 aplicada a los pesos de las hojas.

   • Recomendación:

     • Comenzar con valores entre 3 y 10.

     • Incrementar si se observa sobreajuste.

5. bagging_temperature:

   • Controla la aleatoriedad en la selección de muestras para cada iteración.

   • Recomendación:

     • Usar valores bajos (0.0 a 1.0) para datos grandes.

     • Incrementar para datos más pequeños o ruidosos.

6. border_count:

   • Define el número de divisiones utilizadas para variables numéricas.

   • Recomendación:

     • Comenzar con 128.

     • Reducir para conjuntos de datos pequeños o si el tiempo de entrenamiento es un problema.

Estrategia para el ajuste de hiperparámetros

1. Comenzar con configuraciones predeterminadas:

   • CatBoost tiene parámetros predeterminados sólidos que funcionan bien en muchos problemas.

2. Ajustar iteraciones y learning_rate:

   • Usar un valor bajo de learning_rate (0.01 a 0.1) y aumentar las iteraciones para lograr un ajuste más preciso.

3. Probar diferentes profundidades (depth):

   • Probar valores entre 6 y 10 dependiendo de la complejidad de los datos.

4. Usar validación cruzada:

   • Evaluar el modelo en varias particiones para detectar sobreajuste o subajuste.

5. Explorar con Grid Search o Random Search:

   • Probar combinaciones de parámetros clave como depth, l2_leaf_reg, y bagging_temperature.

Modelo con CatBoostRegressor.

catboost = CatBoostRegressor(
    loss_function="RMSE",
    cat_features=features_categoric,
    learning_rate=0.0075,
    n_estimators=2500,
    min_child_samples=150,
    reg_lambda=150,
    max_depth=3,
    subsample=0.8,
    random_state=42,
    verbose=0
)

catboost.fit(X_train, y_train)

print(f"\nR2 en Train: {catboost.score(X_train, y_train):.4f}")
print(f"R2 en Test:  {catboost.score(X_test, y_test):.4f}")

df_plot = pd.DataFrame({"true_value": y_test, "predicted": catboost.predict(X_test)})
px.scatter(df_plot, x="true_value", y="predicted")

cv_catboost = cross_validate(
    catboost,
    X_train,
    y_train,
    cv=5,
    scoring="r2",
    return_train_score=True
)

plt.plot(cv_catboost["train_score"], label=f"Train. MAE in Train: {cv_catboost['train_score'].mean():.4f}")
plt.plot(cv_catboost["test_score"], label=f"Test.  MAE in Test:  {cv_catboost['test_score'].mean():.4f}")
plt.title("CatBoostRegressor - 5-fold cross-validation")
plt.legend()
plt.xticks(np.arange(0,5), np.arange(1,6))
plt.show()

R2 en Train: 0.8137
R2 en Test:  0.8018

pipeline_catboost = Pipeline(steps=[
    ("model", catboost)
])

pipeline_catboost.fit(X_train, y_train)

print(pipeline_catboost.score(X_train, y_train))
print(pipeline_catboost.score(X_test, y_test))

0.8137006861318932
0.8017986205122258

catboost = CatBoostRegressor(cat_features=features_categoric, verbose=0)

pipeline_catboost = Pipeline(steps=[
    ("model", catboost)
])

param_distributions = {
    "model__iterations": [100, 300, 500, 1000, 2000],
    "model__learning_rate": [0.005, 0.075, 0.01, 0.02, 0.05],
    "model__depth": [4, 6, 8, 10],
    "model__l2_leaf_reg": [1, 3, 5, 7, 9],
    "model__bagging_temperature": [0, 0.1, 0.2, 0.5, 1.0],
    "model__loss_function": ["MAE", "RMSE", "MAPE"]
}

random_search_catboost = RandomizedSearchCV(
    estimator=pipeline_catboost,
    param_distributions=param_distributions,
    n_iter=10,
    scoring="r2",
    cv=5,
    verbose=3,
    random_state=42,
    n_jobs=-1,
    return_train_score=True
)

random_search_catboost.fit(X_train, y_train)

print("Best parameters found: ", random_search_catboost.best_params_)
print("Best CV score R2: ", random_search_catboost.best_score_)

best_model = random_search_catboost.best_estimator_
print(best_model.score(X_train, y_train))
print(best_model.score(X_test, y_test))

pd.DataFrame(random_search_catboost.cv_results_).to_parquet("data/catboost_cv_search.parquet")

Fitting 5 folds for each of 50 candidates, totalling 250 fits
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[CV 2/5] END model__bagging_temperature=0, model__depth=8, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.294, test=0.283) total time=  29.3s
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[CV 3/5] END model__bagging_temperature=0, model__depth=8, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.292, test=0.264) total time=  30.5s
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[CV 1/5] END model__bagging_temperature=0, model__depth=8, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.384, test=0.347) total time=  31.0s
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.888, test=0.821) total time=  39.1s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.891, test=0.862) total time=  40.4s107:	learn: 0.2735020	total: 9.74s	remaining: 17.3s

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[CV 5/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.903, test=0.874) total time=  44.9s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.896, test=0.880) total time=  46.2s
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.906, test=0.836) total time=  50.1s
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[CV 4/5] END model__bagging_temperature=0, model__depth=8, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.307, test=0.331) total time=  28.2s
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[CV 5/5] END model__bagging_temperature=0, model__depth=8, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.335, test=0.344) total time=  27.7s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.382, test=0.327) total time=  28.6s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.290, test=0.285) total time=  28.7s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.323, test=0.298) total time=  27.6s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.312, test=0.325) total time=  28.1s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.210, test=0.208) total time=  27.0s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.918, test=0.872) total time= 1.8min
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.914, test=0.879) total time= 1.8min
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66:	learn: 304675.8842426	total: 3.53s	remaining: 1m 41s
18:	learn: 354244.5395752	total: 1.07s	remaining: 1m 52s
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21:	learn: 351089.3311130	total: 1.18s	remaining: 1m 46s
[CV 3/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.916, test=0.894) total time= 1.8min
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28:	learn: 343927.8944272	total: 1.51s	remaining: 1m 42s
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0:	learn: 364539.2830820	total: 56.5ms	remaining: 5.59s
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1864:	learn: 60261.1213525	total: 2m 1s	remaining: 8.79s
74:	learn: 298275.9376054	total: 4.04s	remaining: 1m 43s
29:	learn: 342909.3455464	total: 1.57s	remaining: 1m 43s
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1735:	learn: 61462.1534962	total: 1m 53s	remaining: 17.3s
1:	learn: 362242.0597216	total: 127ms	remaining: 6.23s
75:	learn: 297502.5404736	total: 4.07s	remaining: 1m 43s
30:	learn: 341916.8388499	total: 1.62s	remaining: 1m 42s
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1736:	learn: 61457.3232691	total: 1m 54s	remaining: 17.3s
2:	learn: 359847.7262602	total: 191ms	remaining: 6.16s
[CV 1/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.919, test=0.875) total time= 2.2min
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3:	learn: 357505.3524173	total: 234ms	remaining: 5.61s
31:	learn: 340941.0242687	total: 1.7s	remaining: 1m 44s
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1737:	learn: 61452.3296051	total: 1m 54s	remaining: 17.2s
77:	learn: 295916.9598624	total: 4.2s	remaining: 1m 43s
1940:	learn: 59985.3388599	total: 2m 4s	remaining: 3.78s
4:	learn: 355131.5568402	total: 293ms	remaining: 5.58s
1902:	learn: 59790.4067356	total: 2m 3s	remaining: 6.31s
32:	learn: 339905.0149623	total: 1.76s	remaining: 1m 45s
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5:	learn: 352782.8608771	total: 325ms	remaining: 5.09s
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33:	learn: 338963.0331039	total: 1.82s	remaining: 1m 45s
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1903:	learn: 59786.8205628	total: 2m 3s	remaining: 6.25s
78:	learn: 295128.1359361	total: 4.31s	remaining: 1m 44s
6:	learn: 350441.1767138	total: 401ms	remaining: 5.32s
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7:	learn: 348155.9488924	total: 428ms	remaining: 4.93s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.916, test=0.897) total time= 2.2min
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.663, test=0.601) total time=   6.5s
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1:	learn: 365293.2160858	total: 138ms	remaining: 6.75s
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1841:	learn: 60935.8602964	total: 2m	remaining: 10.4s
[CV 2/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.658, test=0.627) total time=   6.6s
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3:	learn: 360535.6669943	total: 226ms	remaining: 5.43s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.915, test=0.897) total time= 2.2min
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.914, test=0.904) total time= 2.2min
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.656, test=0.642) total time=   6.3s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.655, test=0.666) total time=   6.2s
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.660, test=0.649) total time=   6.3s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.916, test=0.897) total time= 2.2min
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=-0.186, test=-0.164) total time=  14.5s
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=-0.224, test=-0.210) total time=  14.4s
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=-0.222, test=-0.237) total time=  14.0s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=-0.224, test=-0.218) total time=  14.2s
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=-0.194, test=-0.226) total time=  14.5s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.953, test=0.870) total time=  43.4s
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.918, test=0.885) total time=  40.4s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.958, test=0.866) total time=  50.4s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.957, test=0.906) total time=  47.0s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.956, test=0.899) total time=  45.6s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.959, test=0.901) total time=  48.9s
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[CV 1/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.759, test=0.695) total time=  14.0s
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[CV 2/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.752, test=0.732) total time=  13.9s
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.915, test=0.888) total time=  31.2s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.918, test=0.895) total time=  29.9s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.915, test=0.899) total time=  29.8s
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67:	learn: 307884.2039127	total: 3.33s	remaining: 11.4s
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19:	learn: 170455.4208363	total: 1.64s	remaining: 23s
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68:	learn: 307027.6188781	total: 3.35s	remaining: 11.2s
289:	learn: 187477.5076707	total: 13.4s	remaining: 462ms
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1941:	learn: 108717.9115783	total: 1m 44s	remaining: 3.12s
290:	learn: 187162.7400079	total: 13.4s	remaining: 415ms
20:	learn: 168235.6428010	total: 1.7s	remaining: 22.5s
1910:	learn: 110162.5364532	total: 1m 41s	remaining: 4.75s
27:	learn: 156410.9689805	total: 2.04s	remaining: 19.8s
69:	learn: 306274.0534655	total: 3.4s	remaining: 11.2s
291:	learn: 186876.4034679	total: 13.5s	remaining: 369ms
293:	learn: 187308.8507843	total: 13.4s	remaining: 273ms
292:	learn: 186569.8753906	total: 13.5s	remaining: 322ms
1942:	learn: 108709.3880866	total: 1m 44s	remaining: 3.07s
70:	learn: 305427.1131082	total: 3.43s	remaining: 11.1s
28:	learn: 154674.6497647	total: 2.08s	remaining: 19.5s
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293:	learn: 186260.8256491	total: 13.5s	remaining: 276ms
21:	learn: 166256.6542949	total: 1.77s	remaining: 22.4s
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71:	learn: 304610.9232211	total: 3.48s	remaining: 11s
294:	learn: 186992.6752513	total: 13.5s	remaining: 228ms
[CV 5/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.917, test=0.890) total time=  28.5s
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294:	learn: 185945.7865145	total: 13.5s	remaining: 229ms
22:	learn: 164271.0176516	total: 1.81s	remaining: 21.9s
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23:	learn: 162303.6046339	total: 1.87s	remaining: 21.5s
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297:	learn: 186112.3024185	total: 13.5s	remaining: 90.9ms
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24:	learn: 160317.9254584	total: 1.93s	remaining: 21.3s
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76:	learn: 300527.2434549	total: 3.68s	remaining: 10.7s
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32:	learn: 148131.7138369	total: 2.35s	remaining: 19s
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26:	learn: 156698.5252742	total: 2.02s	remaining: 20.5s
77:	learn: 299702.4104202	total: 3.73s	remaining: 10.6s
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1920:	learn: 110051.4553856	total: 1m 42s	remaining: 4.21s
33:	learn: 146427.6446082	total: 2.4s	remaining: 18.7s
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78:	learn: 298948.0932856	total: 3.79s	remaining: 10.6s
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27:	learn: 154987.4508761	total: 2.13s	remaining: 20.7s
79:	learn: 298140.7724619	total: 3.83s	remaining: 10.5s
34:	learn: 144961.0756175	total: 2.48s	remaining: 18.8s
1922:	learn: 110012.3832559	total: 1m 42s	remaining: 4.1s
[CV 4/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.749, test=0.762) total time=  14.0s
28:	learn: 153303.7445368	total: 2.18s	remaining: 20.4s
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80:	learn: 297336.0941161	total: 3.88s	remaining: 10.5s
0:	learn: 218469.7418568	total: 94.5ms	remaining: 28.3s
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35:	learn: 143580.2921226	total: 2.56s	remaining: 18.7s
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29:	learn: 151695.1350526	total: 2.25s	remaining: 20.2s
82:	learn: 295805.4349444	total: 3.94s	remaining: 10.3s
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1953:	learn: 108568.5392021	total: 1m 45s	remaining: 2.47s
83:	learn: 295011.9137269	total: 3.99s	remaining: 10.3s
36:	learn: 142165.6269449	total: 2.63s	remaining: 18.7s
1:	learn: 215563.7024554	total: 211ms	remaining: 31.4s
1925:	learn: 109979.9823321	total: 1m 42s	remaining: 3.94s
84:	learn: 294271.1260554	total: 4.03s	remaining: 10.2s
30:	learn: 149892.8441801	total: 2.34s	remaining: 20.3s
1954:	learn: 108560.6051471	total: 1m 45s	remaining: 2.42s
1926:	learn: 109968.4997972	total: 1m 42s	remaining: 3.89s
85:	learn: 293526.6998611	total: 4.06s	remaining: 10.1s
37:	learn: 140715.4694463	total: 2.71s	remaining: 18.7s
2:	learn: 212694.0376136	total: 278ms	remaining: 27.6s
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86:	learn: 292766.9221131	total: 4.1s	remaining: 10s
31:	learn: 148405.1429386	total: 2.42s	remaining: 20.2s
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38:	learn: 139342.1096726	total: 2.78s	remaining: 18.6s
87:	learn: 292024.7718609	total: 4.14s	remaining: 9.98s
3:	learn: 209741.8140569	total: 361ms	remaining: 26.7s
[CV 3/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.751, test=0.738) total time=  14.3s
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88:	learn: 291260.2468828	total: 4.18s	remaining: 9.91s
32:	learn: 146897.5384879	total: 2.5s	remaining: 20.2s
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0:	learn: 218146.3122011	total: 88.1ms	remaining: 26.3s
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89:	learn: 290533.5480265	total: 4.22s	remaining: 9.84s
39:	learn: 138057.4376567	total: 2.86s	remaining: 18.6s
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33:	learn: 145565.1043808	total: 2.54s	remaining: 19.9s
4:	learn: 206785.1576113	total: 444ms	remaining: 26.2s
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40:	learn: 136679.7969983	total: 2.93s	remaining: 18.5s
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1:	learn: 215356.5258835	total: 190ms	remaining: 28.4s
5:	learn: 204027.2868012	total: 525ms	remaining: 25.7s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.916, test=0.900) total time= 1.8min
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.916, test=0.888) total time= 1.8min
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[CV 5/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.754, test=0.744) total time=  14.8s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.884, test=0.821) total time=  23.8s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.875, test=0.852) total time=  24.8s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.876, test=0.850) total time=  24.2s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.867, test=0.876) total time=  24.8s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=300, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.872, test=0.873) total time=  24.7s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.980, test=0.889) total time= 2.8min
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.982, test=0.912) total time= 2.8min
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.980, test=0.891) total time= 2.8min
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.849, test=0.794) total time=  44.3s
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=10, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.949, test=0.865) total time= 4.6min
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=10, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.948, test=0.866) total time= 4.6min
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.839, test=0.829) total time=  45.0s
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=10, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.950, test=0.897) total time= 4.6min
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.614, test=0.583) total time=   4.7s
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.612, test=0.578) total time=   4.5s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=10, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.948, test=0.900) total time= 4.6min
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=10, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.950, test=0.898) total time= 4.6min
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.627, test=0.613) total time=   4.4s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.607, test=0.635) total time=   4.5s
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.614, test=0.665) total time=   4.3s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.835, test=0.825) total time=  41.0s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.830, test=0.853) total time=  39.4s
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.744, test=0.718) total time=  19.0s
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.760, test=0.709) total time=  19.5s
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.745, test=0.736) total time=  19.5s
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=4, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.839, test=0.854) total time=  40.0s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.736, test=0.769) total time=  21.2s
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.742, test=0.786) total time=  21.4s
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[CV 1/5] END model__bagging_temperature=0, model__depth=4, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.878, test=0.838) total time=  10.1s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.981, test=0.916) total time= 2.7min
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=3, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.982, test=0.911) total time= 2.8min
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[CV 2/5] END model__bagging_temperature=0, model__depth=4, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.844, test=0.823) total time=  10.4s
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[CV 3/5] END model__bagging_temperature=0, model__depth=4, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.854, test=0.833) total time=  10.2s
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[CV 4/5] END model__bagging_temperature=0, model__depth=4, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.853, test=0.857) total time=   9.8s
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[CV 5/5] END model__bagging_temperature=0, model__depth=4, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.853, test=0.836) total time=  10.1s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.083, test=0.076) total time=  19.4s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.105, test=-0.103) total time=  19.6s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.162, test=-0.175) total time=  19.3s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.121, test=-0.108) total time=  19.6s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.002, test=-0.014) total time=  20.8s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.954, test=0.873) total time= 2.3min
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.953, test=0.910) total time= 2.3min
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.955, test=0.901) total time= 2.3min
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.955, test=0.893) total time= 2.3min
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[CV 1/5] END model__bagging_temperature=0, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.923, test=0.859) total time=   6.4s
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[CV 2/5] END model__bagging_temperature=0, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.916, test=0.864) total time=   6.3s
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[CV 3/5] END model__bagging_temperature=0, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.918, test=0.892) total time=   6.0s
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[CV 4/5] END model__bagging_temperature=0, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.917, test=0.896) total time=   5.8s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=9, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.956, test=0.904) total time= 2.3min
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[CV 5/5] END model__bagging_temperature=0, model__depth=8, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.919, test=0.889) total time=   5.3s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.769, test=0.718) total time=  27.2s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.768, test=0.736) total time=  26.5s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.765, test=0.743) total time=  27.1s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.759, test=0.783) total time=  26.8s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.757, test=0.793) total time=  27.2s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.902, test=0.847) total time= 2.2min
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1996:	learn: 42132.7953187	total: 4m 32s	remaining: 409ms
[CV 3/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.907, test=0.881) total time= 2.3min
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8:	learn: 365912.7604140	total: 653ms	remaining: 2m 24s
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1890:	learn: 43174.2638419	total: 4m 19s	remaining: 14.9s
1528:	learn: 46650.0995545	total: 3m 28s	remaining: 1m 4s
1963:	learn: 106074.8219119	total: 2m 39s	remaining: 2.93s
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12:	learn: 361627.3016469	total: 1.04s	remaining: 2m 38s
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13:	learn: 360516.5245263	total: 1.18s	remaining: 2m 47s
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1515:	learn: 47095.1253447	total: 3m 26s	remaining: 1m 5s
1965:	learn: 106045.9005017	total: 2m 39s	remaining: 2.76s
[CV 1/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.953, test=0.875) total time= 4.6min
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14:	learn: 359433.3660221	total: 1.26s	remaining: 2m 46s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.919, test=0.857) total time= 2.7min
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.955, test=0.892) total time= 4.6min
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.955, test=0.902) total time= 4.6min
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=500, model__l2_leaf_reg=7, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.955, test=0.885) total time=  39.3s631:	learn: 144157.2481018	total: 50.8s	remaining: 1m 49s

[CV 1/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=500, model__l2_leaf_reg=7, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.957, test=0.875) total time=  42.8s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=500, model__l2_leaf_reg=7, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.957, test=0.907) total time=  39.6s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=500, model__l2_leaf_reg=7, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.956, test=0.914) total time=  39.6s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.955, test=0.910) total time= 4.6min
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=10, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.956, test=0.904) total time= 4.6min
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=500, model__l2_leaf_reg=7, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.954, test=0.904) total time=  39.6s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAE;, score=(train=0.951, test=0.888) total time=  44.8s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.425, test=0.386) total time=   5.8s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.423, test=0.401) total time=   6.1s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.419, test=0.411) total time=   5.8s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.420, test=0.432) total time=   6.0s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=100, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.420, test=0.427) total time=   5.8s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAE;, score=(train=0.952, test=0.895) total time=  45.6s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAE;, score=(train=0.951, test=0.904) total time=  44.1s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAE;, score=(train=0.950, test=0.909) total time=  44.3s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=MAE;, score=(train=0.951, test=0.905) total time=  44.2s
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.245, test=0.237) total time=   5.0s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.908, test=0.878) total time= 2.1min
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.247, test=0.227) total time=   4.4s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAPE;, score=(train=0.104, test=0.109) total time=  27.2s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.005, model__loss_function=RMSE;, score=(train=0.908, test=0.892) total time= 2.3min
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAPE;, score=(train=0.055, test=0.035) total time=  27.4s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAPE;, score=(train=0.161, test=0.148) total time=  29.1s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.242, test=0.234) total time=   4.7s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.237, test=0.249) total time=   4.5s
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.240, test=0.267) total time=   4.5s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAPE;, score=(train=0.097, test=0.114) total time=  29.1s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.754, test=0.688) total time=   3.0s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.721, test=0.715) total time=   3.1s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAPE;, score=(train=0.125, test=0.119) total time=  27.8s
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[CV 1/5] END model__bagging_temperature=0, model__depth=10, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.05, model__loss_function=MAPE;, score=(train=0.382, test=0.349) total time=   9.9s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.730, test=0.718) total time=   2.8s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.738, test=0.763) total time=   2.9s
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[CV 2/5] END model__bagging_temperature=0, model__depth=10, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.05, model__loss_function=MAPE;, score=(train=0.367, test=0.366) total time=   9.9s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.739, test=0.731) total time=   2.8s
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[CV 4/5] END model__bagging_temperature=0, model__depth=10, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.05, model__loss_function=MAPE;, score=(train=0.349, test=0.378) total time=  10.1s
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[CV 5/5] END model__bagging_temperature=0, model__depth=10, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.05, model__loss_function=MAPE;, score=(train=0.364, test=0.343) total time=  10.2s
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.813, test=0.752) total time=   4.1s
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.810, test=0.793) total time=   3.6s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.815, test=0.821) total time=   4.0s
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.814, test=0.792) total time=   4.2s
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=6, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.02, model__loss_function=RMSE;, score=(train=0.815, test=0.789) total time=   4.1s
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[CV 3/5] END model__bagging_temperature=0, model__depth=10, model__iterations=100, model__l2_leaf_reg=7, model__learning_rate=0.05, model__loss_function=MAPE;, score=(train=0.378, test=0.336) total time=  10.2s
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.414, test=0.397) total time=   4.5s
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.431, test=0.402) total time=   5.0s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.423, test=0.414) total time=   5.1s
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.414, test=0.433) total time=   4.8s
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.416, test=0.454) total time=   4.5s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.951, test=0.899) total time= 1.3min
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.950, test=0.901) total time= 1.3min
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1186:	learn: 108858.2569059	total: 1m 29s	remaining: 1m 1s
[CV 3/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.951, test=0.909) total time= 1.3min
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.927, test=0.866) total time= 2.5min
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[CV 5/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.932, test=0.894) total time= 2.6min
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.931, test=0.904) total time= 2.7min
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[CV 3/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.930, test=0.897) total time= 2.7min
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.951, test=0.909) total time= 1.6min
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.05, model__loss_function=RMSE;, score=(train=0.953, test=0.906) total time= 1.6min
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[CV 1/5] END model__bagging_temperature=0.2, model__depth=8, model__iterations=2000, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.938, test=0.871) total time= 3.3min
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[CV 1/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.848, test=0.790) total time=  25.9s
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[CV 2/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.837, test=0.823) total time=  25.4s
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[CV 3/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.839, test=0.823) total time=  24.3s
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[CV 4/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.832, test=0.852) total time=  23.3s
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[CV 5/5] END model__bagging_temperature=0, model__depth=6, model__iterations=300, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.834, test=0.857) total time=  24.4s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.955, test=0.879) total time= 3.0min
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.956, test=0.891) total time= 2.7min
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.955, test=0.899) total time= 2.7min
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640:	learn: 0.3089068	total: 53.5s	remaining: 1m 53s
[CV 4/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.954, test=0.911) total time= 2.7min
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=1000, model__l2_leaf_reg=7, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.957, test=0.905) total time= 2.7min
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[CV 1/5] END model__bagging_temperature=0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.250, test=0.227) total time= 2.9min
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[CV 2/5] END model__bagging_temperature=0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.258, test=0.252) total time= 2.9min
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[CV 3/5] END model__bagging_temperature=0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.264, test=0.239) total time= 2.9min
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.896, test=0.848) total time=  26.6s
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[CV 4/5] END model__bagging_temperature=0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.271, test=0.287) total time= 2.8min
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.889, test=0.855) total time=  25.0s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.891, test=0.871) total time=  26.8s
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[CV 5/5] END model__bagging_temperature=0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=5, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.274, test=0.280) total time= 2.8min
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454:	learn: 127220.5768232	total: 26.4s	remaining: 2.61s
30:	learn: 0.3600347	total: 4.64s	remaining: 40.3s
1919:	learn: 0.2750330	total: 2m 43s	remaining: 6.83s
1122:	learn: 0.2822926	total: 1m 35s	remaining: 1m 14s
1987:	learn: 0.2717247	total: 2m 51s	remaining: 1.03s
1044:	learn: 0.2893590	total: 1m 28s	remaining: 1m 20s
455:	learn: 127152.5345783	total: 26.4s	remaining: 2.55s
1920:	learn: 0.2750280	total: 2m 43s	remaining: 6.74s
456:	learn: 127087.8411023	total: 26.5s	remaining: 2.49s
1123:	learn: 0.2822835	total: 1m 35s	remaining: 1m 14s
1045:	learn: 0.2893403	total: 1m 28s	remaining: 1m 20s
457:	learn: 127078.1854763	total: 26.5s	remaining: 2.43s
1988:	learn: 0.2717242	total: 2m 51s	remaining: 948ms
1937:	learn: 0.2726182	total: 2m 46s	remaining: 5.34s
1921:	learn: 0.2750272	total: 2m 43s	remaining: 6.65s
31:	learn: 0.3595965	total: 4.76s	remaining: 39.9s
458:	learn: 127013.3200346	total: 26.5s	remaining: 2.37s
1124:	learn: 0.2822781	total: 1m 35s	remaining: 1m 14s
1938:	learn: 0.2726158	total: 2m 46s	remaining: 5.25s
1046:	learn: 0.2893357	total: 1m 28s	remaining: 1m 20s
[CV 4/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.891, test=0.886) total time=  30.8s
1922:	learn: 0.2750258	total: 2m 44s	remaining: 6.57s
1989:	learn: 0.2717175	total: 2m 51s	remaining: 862ms
459:	learn: 126945.2868733	total: 26.6s	remaining: 2.31s
32:	learn: 0.3591960	total: 4.87s	remaining: 39.4s
1125:	learn: 0.2822495	total: 1m 35s	remaining: 1m 14s
1939:	learn: 0.2726136	total: 2m 47s	remaining: 5.16s
1923:	learn: 0.2750238	total: 2m 44s	remaining: 6.48s
1047:	learn: 0.2893325	total: 1m 28s	remaining: 1m 20s
460:	learn: 126877.9530814	total: 26.6s	remaining: 2.25s
1990:	learn: 0.2717157	total: 2m 51s	remaining: 775ms
461:	learn: 126809.8572127	total: 26.7s	remaining: 2.19s
1924:	learn: 0.2750174	total: 2m 44s	remaining: 6.4s
1048:	learn: 0.2891814	total: 1m 28s	remaining: 1m 20s
1940:	learn: 0.2726075	total: 2m 47s	remaining: 5.08s
1126:	learn: 0.2822336	total: 1m 36s	remaining: 1m 14s
462:	learn: 126742.0410134	total: 26.7s	remaining: 2.14s
1991:	learn: 0.2717117	total: 2m 51s	remaining: 689ms
1127:	learn: 0.2822320	total: 1m 36s	remaining: 1m 14s
1049:	learn: 0.2891677	total: 1m 28s	remaining: 1m 20s
463:	learn: 126672.3332286	total: 26.8s	remaining: 2.08s
1941:	learn: 0.2726060	total: 2m 47s	remaining: 4.99s
1925:	learn: 0.2750166	total: 2m 44s	remaining: 6.31s
1992:	learn: 0.2717107	total: 2m 51s	remaining: 603ms
1050:	learn: 0.2891544	total: 1m 28s	remaining: 1m 20s
464:	learn: 126609.5810367	total: 26.9s	remaining: 2.02s
33:	learn: 0.3586693	total: 5.14s	remaining: 40.2s
1926:	learn: 0.2750040	total: 2m 44s	remaining: 6.23s
1942:	learn: 0.2726042	total: 2m 47s	remaining: 4.91s
1993:	learn: 0.2717095	total: 2m 51s	remaining: 517ms
1128:	learn: 0.2822144	total: 1m 36s	remaining: 1m 14s
465:	learn: 126551.6168962	total: 26.9s	remaining: 1.96s
1051:	learn: 0.2891501	total: 1m 28s	remaining: 1m 20s
466:	learn: 126477.3255826	total: 27s	remaining: 1.9s
1943:	learn: 0.2726014	total: 2m 47s	remaining: 4.82s
1129:	learn: 0.2822014	total: 1m 36s	remaining: 1m 14s
467:	learn: 126395.8699932	total: 27s	remaining: 1.84s
1994:	learn: 0.2717085	total: 2m 51s	remaining: 431ms
34:	learn: 0.3581448	total: 5.28s	remaining: 40s
1927:	learn: 0.2750010	total: 2m 44s	remaining: 6.14s
1130:	learn: 0.2822007	total: 1m 36s	remaining: 1m 14s
0:	learn: 0.3767958	total: 228ms	remaining: 1m 8s
1944:	learn: 0.2726001	total: 2m 47s	remaining: 4.73s
1052:	learn: 0.2891445	total: 1m 29s	remaining: 1m 20s
468:	learn: 126310.4188829	total: 27.1s	remaining: 1.79s
1995:	learn: 0.2717065	total: 2m 51s	remaining: 345ms
1928:	learn: 0.2749917	total: 2m 44s	remaining: 6.06s
1131:	learn: 0.2821881	total: 1m 36s	remaining: 1m 13s
1053:	learn: 0.2891185	total: 1m 29s	remaining: 1m 19s
1996:	learn: 0.2717049	total: 2m 52s	remaining: 258ms
1945:	learn: 0.2725980	total: 2m 47s	remaining: 4.65s
469:	learn: 126246.6789762	total: 27.2s	remaining: 1.73s
1:	learn: 0.3761124	total: 345ms	remaining: 51.4s
470:	learn: 126164.6532395	total: 27.2s	remaining: 1.67s
1997:	learn: 0.2717035	total: 2m 52s	remaining: 172ms
1929:	learn: 0.2749909	total: 2m 44s	remaining: 5.97s
35:	learn: 0.3576567	total: 5.49s	remaining: 40.2s
1946:	learn: 0.2725971	total: 2m 47s	remaining: 4.56s
471:	learn: 126111.8518188	total: 27.2s	remaining: 1.62s
1132:	learn: 0.2821871	total: 1m 36s	remaining: 1m 13s
1054:	learn: 0.2891136	total: 1m 29s	remaining: 1m 19s
472:	learn: 126063.6804918	total: 27.3s	remaining: 1.56s
1930:	learn: 0.2749907	total: 2m 44s	remaining: 5.89s
1998:	learn: 0.2716929	total: 2m 52s	remaining: 86.1ms
2:	learn: 0.3754883	total: 496ms	remaining: 49.1s
1133:	learn: 0.2821831	total: 1m 36s	remaining: 1m 13s
473:	learn: 126021.3190767	total: 27.3s	remaining: 1.5s
1947:	learn: 0.2725949	total: 2m 47s	remaining: 4.48s
36:	learn: 0.3571636	total: 5.6s	remaining: 39.8s
1055:	learn: 0.2890750	total: 1m 29s	remaining: 1m 19s
1931:	learn: 0.2749880	total: 2m 44s	remaining: 5.8s
474:	learn: 125948.7732871	total: 27.4s	remaining: 1.44s
1134:	learn: 0.2821505	total: 1m 36s	remaining: 1m 13s
1999:	learn: 0.2716886	total: 2m 52s	remaining: 0us
475:	learn: 125940.2771000	total: 27.4s	remaining: 1.38s
1948:	learn: 0.2725946	total: 2m 47s	remaining: 4.39s
1056:	learn: 0.2890705	total: 1m 29s	remaining: 1m 19s
3:	learn: 0.3748442	total: 624ms	remaining: 46.2s
1932:	learn: 0.2749868	total: 2m 44s	remaining: 5.72s
37:	learn: 0.3568352	total: 5.71s	remaining: 39.4s
1135:	learn: 0.2821372	total: 1m 36s	remaining: 1m 13s
476:	learn: 125864.0581786	total: 27.5s	remaining: 1.32s
1949:	learn: 0.2725930	total: 2m 47s	remaining: 4.3s
477:	learn: 125856.1137644	total: 27.5s	remaining: 1.27s
1057:	learn: 0.2890651	total: 1m 29s	remaining: 1m 19s
1136:	learn: 0.2821317	total: 1m 36s	remaining: 1m 13s
1933:	learn: 0.2749792	total: 2m 45s	remaining: 5.63s
38:	learn: 0.3562534	total: 5.8s	remaining: 38.8s
478:	learn: 125791.7723790	total: 27.6s	remaining: 1.21s
4:	learn: 0.3744926	total: 761ms	remaining: 44.9s
1950:	learn: 0.2725909	total: 2m 47s	remaining: 4.22s
1934:	learn: 0.2749780	total: 2m 45s	remaining: 5.54s
1058:	learn: 0.2890612	total: 1m 29s	remaining: 1m 19s
479:	learn: 125726.3034965	total: 27.6s	remaining: 1.15s
1137:	learn: 0.2821274	total: 1m 37s	remaining: 1m 13s
39:	learn: 0.3558255	total: 5.9s	remaining: 38.3s
1951:	learn: 0.2725865	total: 2m 48s	remaining: 4.13s
480:	learn: 125674.9153275	total: 27.7s	remaining: 1.09s
1059:	learn: 0.2890587	total: 1m 29s	remaining: 1m 19s
5:	learn: 0.3739823	total: 895ms	remaining: 43.8s
1935:	learn: 0.2749774	total: 2m 45s	remaining: 5.46s
1138:	learn: 0.2821256	total: 1m 37s	remaining: 1m 13s
481:	learn: 125629.9787670	total: 27.8s	remaining: 1.04s
1952:	learn: 0.2725834	total: 2m 48s	remaining: 4.05s
1936:	learn: 0.2749760	total: 2m 45s	remaining: 5.38s
1060:	learn: 0.2890537	total: 1m 29s	remaining: 1m 19s
1139:	learn: 0.2821202	total: 1m 37s	remaining: 1m 13s
482:	learn: 125585.1032775	total: 27.8s	remaining: 979ms
40:	learn: 0.3552294	total: 6.08s	remaining: 38.4s
483:	learn: 125576.7460701	total: 27.8s	remaining: 920ms
1953:	learn: 0.2725813	total: 2m 48s	remaining: 3.96s
484:	learn: 125515.0393712	total: 27.9s	remaining: 863ms
1061:	learn: 0.2890517	total: 1m 29s	remaining: 1m 19s
1140:	learn: 0.2821180	total: 1m 37s	remaining: 1m 13s
1937:	learn: 0.2749739	total: 2m 45s	remaining: 5.29s
6:	learn: 0.3736492	total: 1.09s	remaining: 45.8s
485:	learn: 125457.7072821	total: 27.9s	remaining: 804ms
1954:	learn: 0.2725769	total: 2m 48s	remaining: 3.87s
486:	learn: 125415.7354045	total: 28s	remaining: 746ms
1062:	learn: 0.2890320	total: 1m 29s	remaining: 1m 19s
1938:	learn: 0.2749658	total: 2m 45s	remaining: 5.2s
41:	learn: 0.3546372	total: 6.24s	remaining: 38.3s
1141:	learn: 0.2820764	total: 1m 37s	remaining: 1m 13s
487:	learn: 125349.6347158	total: 28s	remaining: 689ms
1955:	learn: 0.2725765	total: 2m 48s	remaining: 3.79s
7:	learn: 0.3729072	total: 1.24s	remaining: 45.3s
42:	learn: 0.3541043	total: 6.33s	remaining: 37.8s
1939:	learn: 0.2749481	total: 2m 45s	remaining: 5.12s
1142:	learn: 0.2820688	total: 1m 37s	remaining: 1m 13s
1063:	learn: 0.2890245	total: 1m 30s	remaining: 1m 19s
488:	learn: 125341.9848892	total: 28.1s	remaining: 632ms
[CV 1/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=0.304, test=0.268) total time= 2.9min
489:	learn: 125288.9332805	total: 28.1s	remaining: 574ms
1956:	learn: 0.2725716	total: 2m 48s	remaining: 3.7s
1143:	learn: 0.2820598	total: 1m 37s	remaining: 1m 12s
1940:	learn: 0.2749351	total: 2m 45s	remaining: 5.04s
8:	learn: 0.3721554	total: 1.37s	remaining: 44.3s
490:	learn: 125208.1780926	total: 28.2s	remaining: 517ms
43:	learn: 0.3536138	total: 6.46s	remaining: 37.6s
1064:	learn: 0.2890162	total: 1m 30s	remaining: 1m 19s
1957:	learn: 0.2725709	total: 2m 48s	remaining: 3.62s
1941:	learn: 0.2748791	total: 2m 45s	remaining: 4.95s
1144:	learn: 0.2820572	total: 1m 37s	remaining: 1m 12s
491:	learn: 125157.0748574	total: 28.2s	remaining: 459ms
1958:	learn: 0.2725702	total: 2m 48s	remaining: 3.53s
44:	learn: 0.3532471	total: 6.54s	remaining: 37.1s
492:	learn: 125094.5280022	total: 28.3s	remaining: 402ms
1145:	learn: 0.2820486	total: 1m 37s	remaining: 1m 12s
1942:	learn: 0.2748781	total: 2m 45s	remaining: 4.86s
9:	learn: 0.3714507	total: 1.49s	remaining: 43.1s
1065:	learn: 0.2889973	total: 1m 30s	remaining: 1m 19s
1959:	learn: 0.2725699	total: 2m 48s	remaining: 3.44s
493:	learn: 125044.9826399	total: 28.3s	remaining: 344ms
1146:	learn: 0.2820441	total: 1m 37s	remaining: 1m 12s
1943:	learn: 0.2748741	total: 2m 45s	remaining: 4.78s
1066:	learn: 0.2889563	total: 1m 30s	remaining: 1m 19s
494:	learn: 125037.8038188	total: 28.4s	remaining: 287ms
45:	learn: 0.3527178	total: 6.66s	remaining: 36.8s
10:	learn: 0.3707673	total: 1.59s	remaining: 41.8s
1960:	learn: 0.2725634	total: 2m 48s	remaining: 3.36s
1147:	learn: 0.2820437	total: 1m 37s	remaining: 1m 12s
495:	learn: 124967.8857624	total: 28.4s	remaining: 229ms
1944:	learn: 0.2748712	total: 2m 45s	remaining: 4.69s
1067:	learn: 0.2889502	total: 1m 30s	remaining: 1m 18s
496:	learn: 124908.8152051	total: 28.5s	remaining: 172ms
1961:	learn: 0.2725608	total: 2m 48s	remaining: 3.27s
11:	learn: 0.3701719	total: 1.7s	remaining: 40.8s
1148:	learn: 0.2820190	total: 1m 37s	remaining: 1m 12s
1945:	learn: 0.2748688	total: 2m 46s	remaining: 4.61s
46:	learn: 0.3521866	total: 6.83s	remaining: 36.8s
1962:	learn: 0.2725527	total: 2m 48s	remaining: 3.18s
497:	learn: 124856.2642533	total: 28.6s	remaining: 115ms
1149:	learn: 0.2820160	total: 1m 37s	remaining: 1m 12s
1946:	learn: 0.2748645	total: 2m 46s	remaining: 4.52s
1068:	learn: 0.2889398	total: 1m 30s	remaining: 1m 18s
498:	learn: 124786.2398353	total: 28.6s	remaining: 57.4ms
12:	learn: 0.3695423	total: 1.82s	remaining: 40.3s
499:	learn: 124734.6957476	total: 28.7s	remaining: 0us
1963:	learn: 0.2725514	total: 2m 49s	remaining: 3.1s
0:	learn: 0.3748937	total: 132ms	remaining: 39.5s
1150:	learn: 0.2820057	total: 1m 38s	remaining: 1m 12s
47:	learn: 0.3517622	total: 6.97s	remaining: 36.6s
1947:	learn: 0.2748606	total: 2m 46s	remaining: 4.44s
1964:	learn: 0.2725498	total: 2m 49s	remaining: 3.01s
13:	learn: 0.3688933	total: 1.94s	remaining: 39.7s
1069:	learn: 0.2889271	total: 1m 30s	remaining: 1m 18s
1151:	learn: 0.2819936	total: 1m 38s	remaining: 1m 12s
1:	learn: 0.3742106	total: 241ms	remaining: 35.9s
1948:	learn: 0.2748402	total: 2m 46s	remaining: 4.35s
1965:	learn: 0.2725468	total: 2m 49s	remaining: 2.93s
48:	learn: 0.3512869	total: 7.1s	remaining: 36.4s
1152:	learn: 0.2819857	total: 1m 38s	remaining: 1m 12s
14:	learn: 0.3681880	total: 2.1s	remaining: 39.9s
1966:	learn: 0.2725458	total: 2m 49s	remaining: 2.84s
1949:	learn: 0.2748394	total: 2m 46s	remaining: 4.27s
1070:	learn: 0.2889256	total: 1m 30s	remaining: 1m 18s
1153:	learn: 0.2819793	total: 1m 38s	remaining: 1m 12s
2:	learn: 0.3735614	total: 427ms	remaining: 42.3s
49:	learn: 0.3509742	total: 7.25s	remaining: 36.3s
1967:	learn: 0.2725416	total: 2m 49s	remaining: 2.75s
15:	learn: 0.3675270	total: 2.21s	remaining: 39.2s
1071:	learn: 0.2889207	total: 1m 31s	remaining: 1m 18s
1950:	learn: 0.2748354	total: 2m 46s	remaining: 4.18s
1154:	learn: 0.2819764	total: 1m 38s	remaining: 1m 12s
3:	learn: 0.3729859	total: 532ms	remaining: 39.3s
1968:	learn: 0.2725407	total: 2m 49s	remaining: 2.67s
1951:	learn: 0.2748274	total: 2m 46s	remaining: 4.1s
[CV 5/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=500, model__l2_leaf_reg=3, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.890, test=0.868) total time=  29.3s
1072:	learn: 0.2889144	total: 1m 31s	remaining: 1m 18s
50:	learn: 0.3503034	total: 7.4s	remaining: 36.1s
1155:	learn: 0.2819484	total: 1m 38s	remaining: 1m 11s
16:	learn: 0.3669578	total: 2.35s	remaining: 39.1s
1969:	learn: 0.2725270	total: 2m 49s	remaining: 2.58s
1970:	learn: 0.2725249	total: 2m 49s	remaining: 2.5s
51:	learn: 0.3499462	total: 7.5s	remaining: 35.8s
1952:	learn: 0.2748263	total: 2m 46s	remaining: 4.01s
1073:	learn: 0.2889046	total: 1m 31s	remaining: 1m 18s
17:	learn: 0.3666817	total: 2.49s	remaining: 39s
1971:	learn: 0.2725239	total: 2m 49s	remaining: 2.41s
1156:	learn: 0.2819325	total: 1m 38s	remaining: 1m 11s
4:	learn: 0.3725167	total: 764ms	remaining: 45.1s
1074:	learn: 0.2888522	total: 1m 31s	remaining: 1m 18s
52:	learn: 0.3493930	total: 7.61s	remaining: 35.5s
1953:	learn: 0.2748246	total: 2m 46s	remaining: 3.93s
1972:	learn: 0.2725195	total: 2m 49s	remaining: 2.32s
1157:	learn: 0.2819077	total: 1m 38s	remaining: 1m 11s
18:	learn: 0.3661657	total: 2.63s	remaining: 38.9s
1954:	learn: 0.2748244	total: 2m 46s	remaining: 3.84s
1973:	learn: 0.2725184	total: 2m 49s	remaining: 2.24s
5:	learn: 0.3721754	total: 929ms	remaining: 45.5s
1158:	learn: 0.2819032	total: 1m 38s	remaining: 1m 11s
1075:	learn: 0.2888501	total: 1m 31s	remaining: 1m 18s
53:	learn: 0.3489675	total: 7.76s	remaining: 35.4s
1974:	learn: 0.2725133	total: 2m 49s	remaining: 2.15s
19:	learn: 0.3656565	total: 2.74s	remaining: 38.4s
1955:	learn: 0.2748193	total: 2m 47s	remaining: 3.76s
1076:	learn: 0.2887502	total: 1m 31s	remaining: 1m 18s
6:	learn: 0.3718218	total: 1.03s	remaining: 43.2s
1159:	learn: 0.2819011	total: 1m 38s	remaining: 1m 11s
54:	learn: 0.3486290	total: 7.86s	remaining: 35s
1975:	learn: 0.2725121	total: 2m 49s	remaining: 2.06s
1956:	learn: 0.2748178	total: 2m 47s	remaining: 3.67s
1077:	learn: 0.2887302	total: 1m 31s	remaining: 1m 18s
0:	learn: 0.3769418	total: 129ms	remaining: 38.7s
20:	learn: 0.3652165	total: 2.86s	remaining: 38s
1160:	learn: 0.2819007	total: 1m 39s	remaining: 1m 11s
1976:	learn: 0.2725112	total: 2m 50s	remaining: 1.98s
1078:	learn: 0.2887283	total: 1m 31s	remaining: 1m 18s
7:	learn: 0.3712284	total: 1.17s	remaining: 42.8s
1957:	learn: 0.2748123	total: 2m 47s	remaining: 3.59s
55:	learn: 0.3481162	total: 8.02s	remaining: 35s
1161:	learn: 0.2818919	total: 1m 39s	remaining: 1m 11s
21:	learn: 0.3649466	total: 2.97s	remaining: 37.6s
1977:	learn: 0.2725010	total: 2m 50s	remaining: 1.89s
8:	learn: 0.3707485	total: 1.24s	remaining: 40.2s
1:	learn: 0.3762240	total: 260ms	remaining: 38.8s
1079:	learn: 0.2887155	total: 1m 31s	remaining: 1m 18s
1958:	learn: 0.2748055	total: 2m 47s	remaining: 3.5s
1162:	learn: 0.2818812	total: 1m 39s	remaining: 1m 11s
1978:	learn: 0.2724995	total: 2m 50s	remaining: 1.81s
1080:	learn: 0.2887115	total: 1m 31s	remaining: 1m 18s
9:	learn: 0.3701288	total: 1.35s	remaining: 39s
1959:	learn: 0.2748045	total: 2m 47s	remaining: 3.42s
1163:	learn: 0.2818240	total: 1m 39s	remaining: 1m 11s
2:	learn: 0.3756656	total: 394ms	remaining: 39s
56:	learn: 0.3477668	total: 8.2s	remaining: 34.9s
1979:	learn: 0.2724989	total: 2m 50s	remaining: 1.72s
22:	learn: 0.3646632	total: 3.15s	remaining: 37.9s
1081:	learn: 0.2887051	total: 1m 31s	remaining: 1m 18s
1164:	learn: 0.2818196	total: 1m 39s	remaining: 1m 11s
1960:	learn: 0.2747950	total: 2m 47s	remaining: 3.33s
1980:	learn: 0.2724960	total: 2m 50s	remaining: 1.63s
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26:	learn: 0.3607197	total: 3.64s	remaining: 36.8s
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37:	learn: 0.3594705	total: 5.39s	remaining: 37.2s
16:	learn: 0.3690473	total: 2.67s	remaining: 44.5s
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74:	learn: 0.3419326	total: 10.5s	remaining: 31.5s
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27:	learn: 0.3601684	total: 3.73s	remaining: 36.2s
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38:	learn: 0.3590688	total: 5.51s	remaining: 36.9s
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17:	learn: 0.3686676	total: 2.88s	remaining: 45.1s
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28:	learn: 0.3595733	total: 3.87s	remaining: 36.2s
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1190:	learn: 0.2815195	total: 1m 41s	remaining: 1m 9s
1984:	learn: 0.2746589	total: 2m 49s	remaining: 1.28s
[CV 2/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=0.286, test=0.275) total time= 2.9min
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20:	learn: 0.3675827	total: 3.33s	remaining: 44.3s
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32:	learn: 0.3576649	total: 4.41s	remaining: 35.7s
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2:	learn: 0.3761519	total: 461ms	remaining: 45.7s
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37:	learn: 0.3548324	total: 4.98s	remaining: 34.4s
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1120:	learn: 0.2881778	total: 1m 35s	remaining: 1m 15s
38:	learn: 0.3543873	total: 5.16s	remaining: 34.5s
6:	learn: 0.3743113	total: 950ms	remaining: 39.7s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=0.271, test=0.245) total time= 2.9min
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.144, test=0.132) total time=  42.6s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.147, test=0.142) total time=  41.2s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.165, test=0.143) total time=  42.0s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.107, test=0.117) total time=  41.8s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=7, model__learning_rate=0.005, model__loss_function=MAPE;, score=(train=0.126, test=0.129) total time=  40.8s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=0.288, test=0.308) total time= 2.9min
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=8, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=0.241, test=0.244) total time= 2.9min
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[CV 1/5] END model__bagging_temperature=0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.864, test=0.802) total time= 1.5min
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[CV 2/5] END model__bagging_temperature=0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.852, test=0.831) total time= 1.6min
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[CV 3/5] END model__bagging_temperature=0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.854, test=0.829) total time= 1.6min
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[CV 4/5] END model__bagging_temperature=0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.846, test=0.861) total time= 1.6min
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[CV 5/5] END model__bagging_temperature=0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.851, test=0.863) total time= 1.6min
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.892, test=0.849) total time= 1.6min
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[CV 1/5] END model__bagging_temperature=0, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.850, test=0.798) total time=  26.8s
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[CV 2/5] END model__bagging_temperature=0, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.835, test=0.827) total time=  25.2s
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[CV 3/5] END model__bagging_temperature=0, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.837, test=0.828) total time=  27.1s
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[CV 4/5] END model__bagging_temperature=0, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.831, test=0.853) total time=  27.6s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.887, test=0.878) total time= 1.7min
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.880, test=0.873) total time= 1.7min
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[CV 5/5] END model__bagging_temperature=0, model__depth=4, model__iterations=500, model__l2_leaf_reg=5, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.839, test=0.852) total time=  26.8s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.875, test=0.887) total time= 1.8min
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=4, model__iterations=2000, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.888, test=0.879) total time= 1.8min
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.958, test=0.866) total time=  54.9s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.953, test=0.870) total time=  48.8s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.959, test=0.901) total time=  53.9s
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74:	learn: 0.3343277	total: 2.67s	remaining: 890ms
1114:	learn: 59163.1891453	total: 2m 6s	remaining: 1m 40s
75:	learn: 0.3343273	total: 2.69s	remaining: 851ms
266:	learn: 81310.9090301	total: 41.7s	remaining: 5.15s
1161:	learn: 58795.8658362	total: 2m 13s	remaining: 1m 36s
76:	learn: 0.3341225	total: 2.73s	remaining: 817ms
1048:	learn: 60201.4983929	total: 1m 59s	remaining: 1m 48s
1157:	learn: 58933.0152424	total: 2m 11s	remaining: 1m 35s
1115:	learn: 59148.9711379	total: 2m 6s	remaining: 1m 39s
77:	learn: 0.3338761	total: 2.79s	remaining: 786ms
997:	learn: 61573.5338115	total: 1m 53s	remaining: 1m 53s
1049:	learn: 60193.4442243	total: 1m 59s	remaining: 1m 48s
1162:	learn: 58786.9754443	total: 2m 13s	remaining: 1m 36s
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1158:	learn: 58920.5307319	total: 2m 11s	remaining: 1m 35s
267:	learn: 81105.9591049	total: 41.8s	remaining: 4.99s
[CV 4/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.957, test=0.906) total time=  53.7s
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0:	learn: 0.3772340	total: 63.5ms	remaining: 6.29s
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1:	learn: 0.3771932	total: 106ms	remaining: 5.22s
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3:	learn: 0.3771696	total: 196ms	remaining: 4.71s
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4:	learn: 0.3753322	total: 242ms	remaining: 4.6s
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1123:	learn: 59063.3684490	total: 2m 6s	remaining: 1m 38s
271:	learn: 80708.1748817	total: 42.5s	remaining: 4.38s
1057:	learn: 60111.8867659	total: 2m	remaining: 1m 47s
5:	learn: 0.3753268	total: 301ms	remaining: 4.72s
6:	learn: 0.3752366	total: 341ms	remaining: 4.53s
1005:	learn: 61466.0367626	total: 1m 54s	remaining: 1m 52s
1170:	learn: 58689.8205898	total: 2m 14s	remaining: 1m 35s
7:	learn: 0.3752366	total: 361ms	remaining: 4.15s
1166:	learn: 58827.2435558	total: 2m 11s	remaining: 1m 34s
1124:	learn: 59053.3254246	total: 2m 6s	remaining: 1m 38s
1058:	learn: 60105.2087505	total: 2m	remaining: 1m 47s
8:	learn: 0.3752359	total: 406ms	remaining: 4.11s
9:	learn: 0.3752359	total: 445ms	remaining: 4s
272:	learn: 80548.1490498	total: 42.7s	remaining: 4.22s
1171:	learn: 58676.9313454	total: 2m 14s	remaining: 1m 34s
1125:	learn: 59041.5407636	total: 2m 7s	remaining: 1m 38s
10:	learn: 0.3752369	total: 462ms	remaining: 3.74s
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11:	learn: 0.3752369	total: 499ms	remaining: 3.66s
1059:	learn: 60088.9629325	total: 2m	remaining: 1m 47s
[CV 1/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=0.010, test=0.013) total time=   3.9s
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13:	learn: 0.3743782	total: 585ms	remaining: 3.59s
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15:	learn: 0.3736376	total: 687ms	remaining: 3.6s
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16:	learn: 0.3736243	total: 749ms	remaining: 3.66s
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20:	learn: 0.3728497	total: 930ms	remaining: 3.5s
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1:	learn: 0.3752688	total: 124ms	remaining: 6.09s
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21:	learn: 0.3701056	total: 999ms	remaining: 3.54s
2:	learn: 0.3736489	total: 182ms	remaining: 5.89s
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22:	learn: 0.3701056	total: 1.02s	remaining: 3.43s
3:	learn: 0.3733431	total: 213ms	remaining: 5.12s
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23:	learn: 0.3681804	total: 1.05s	remaining: 3.34s
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4:	learn: 0.3719391	total: 250ms	remaining: 4.75s
25:	learn: 0.3665842	total: 1.09s	remaining: 3.09s
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5:	learn: 0.3719350	total: 275ms	remaining: 4.31s
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26:	learn: 0.3665743	total: 1.13s	remaining: 3.05s
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1012:	learn: 61357.1057226	total: 1m 54s	remaining: 1m 51s
27:	learn: 0.3650673	total: 1.16s	remaining: 2.99s
6:	learn: 0.3718239	total: 343ms	remaining: 4.56s
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28:	learn: 0.3649246	total: 1.2s	remaining: 2.93s
7:	learn: 0.3717906	total: 378ms	remaining: 4.35s
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8:	learn: 0.3714480	total: 391ms	remaining: 3.95s
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9:	learn: 0.3714434	total: 405ms	remaining: 3.64s
29:	learn: 0.3649253	total: 1.24s	remaining: 2.89s
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1066:	learn: 59997.0933535	total: 2m 1s	remaining: 1m 46s
30:	learn: 0.3649258	total: 1.29s	remaining: 2.88s
10:	learn: 0.3714135	total: 487ms	remaining: 3.94s
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11:	learn: 0.3714119	total: 502ms	remaining: 3.68s
12:	learn: 0.3713844	total: 516ms	remaining: 3.45s
1067:	learn: 59985.5739274	total: 2m 1s	remaining: 1m 46s
1013:	learn: 61348.4851498	total: 1m 55s	remaining: 1m 51s
31:	learn: 0.3649230	total: 1.39s	remaining: 2.96s
277:	learn: 79981.9509004	total: 43.6s	remaining: 3.45s
1174:	learn: 58733.4654686	total: 2m 12s	remaining: 1m 33s
13:	learn: 0.3713825	total: 596ms	remaining: 3.66s
14:	learn: 0.3713428	total: 619ms	remaining: 3.5s
32:	learn: 0.3649182	total: 1.45s	remaining: 2.94s
15:	learn: 0.3713456	total: 634ms	remaining: 3.33s
1133:	learn: 58930.7933767	total: 2m 8s	remaining: 1m 37s
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33:	learn: 0.3648949	total: 1.5s	remaining: 2.92s
16:	learn: 0.3712977	total: 699ms	remaining: 3.41s
17:	learn: 0.3712977	total: 726ms	remaining: 3.31s
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34:	learn: 0.3648958	total: 1.55s	remaining: 2.89s
18:	learn: 0.3712977	total: 750ms	remaining: 3.2s
1134:	learn: 58921.0291082	total: 2m 8s	remaining: 1m 37s
35:	learn: 0.3604137	total: 1.58s	remaining: 2.81s
19:	learn: 0.3704797	total: 779ms	remaining: 3.12s
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20:	learn: 0.3704381	total: 810ms	remaining: 3.05s
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36:	learn: 0.3560076	total: 1.62s	remaining: 2.76s
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37:	learn: 0.3560018	total: 1.69s	remaining: 2.75s
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38:	learn: 0.3559993	total: 1.7s	remaining: 2.66s
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22:	learn: 0.3698748	total: 900ms	remaining: 3.01s
39:	learn: 0.3559993	total: 1.71s	remaining: 2.56s
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40:	learn: 0.3559699	total: 1.74s	remaining: 2.5s
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23:	learn: 0.3698748	total: 974ms	remaining: 3.08s
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[CV 2/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.045, test=-0.026) total time=   4.2s
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3:	learn: 0.3775237	total: 185ms	remaining: 4.43s
1160:	learn: 58632.0858734	total: 2m 11s	remaining: 1m 34s
[CV 3/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.089, test=-0.109) total time=   4.0s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=10, model__iterations=300, model__l2_leaf_reg=5, model__learning_rate=0.075, model__loss_function=RMSE;, score=(train=0.956, test=0.899) total time=  48.6s
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1238:	learn: 58029.8059207	total: 2m 19s	remaining: 1m 25s
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[CV 4/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.062, test=-0.043) total time=   4.1s
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1137:	learn: 59121.9255996	total: 2m 9s	remaining: 1m 37s
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1084:	learn: 60369.4310760	total: 2m 2s	remaining: 1m 43s
6:	learn: 0.3767271	total: 515ms	remaining: 36.3s
51:	learn: 0.3612519	total: 2.8s	remaining: 24.2s
1196:	learn: 58195.1019011	total: 2m 15s	remaining: 1m 31s
7:	learn: 0.3767276	total: 558ms	remaining: 34.3s
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53:	learn: 0.3608784	total: 2.9s	remaining: 23.9s
[CV 5/5] END model__bagging_temperature=0.2, model__depth=4, model__iterations=100, model__l2_leaf_reg=1, model__learning_rate=0.075, model__loss_function=MAPE;, score=(train=-0.036, test=-0.060) total time=   4.3s
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[CV 1/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=500, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=-0.074, test=-0.063) total time=  27.0s
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[CV 2/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=500, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=-0.202, test=-0.189) total time=  27.2s
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[CV 3/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=500, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=-0.201, test=-0.215) total time=  27.1s
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[CV 1/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.618, test=0.585) total time=   5.8s
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[CV 2/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.623, test=0.588) total time=   6.3s
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[CV 3/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.619, test=0.608) total time=   5.9s
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[CV 4/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=500, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=-0.148, test=-0.138) total time=  25.9s
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[CV 4/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.610, test=0.639) total time=   5.9s
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[CV 5/5] END model__bagging_temperature=0.5, model__depth=4, model__iterations=500, model__l2_leaf_reg=1, model__learning_rate=0.01, model__loss_function=MAPE;, score=(train=-0.129, test=-0.154) total time=  27.4s
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[CV 5/5] END model__bagging_temperature=0.1, model__depth=6, model__iterations=100, model__l2_leaf_reg=3, model__learning_rate=0.02, model__loss_function=MAE;, score=(train=0.620, test=0.669) total time=   5.7s
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1828:	learn: 53522.9962898	total: 3m 29s	remaining: 19.6s
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189:	learn: 169341.2836920	total: 32.8s	remaining: 19s
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1829:	learn: 53507.1637900	total: 3m 29s	remaining: 19.5s
1894:	learn: 52555.6522025	total: 3m 36s	remaining: 12s
174:	learn: 178956.1122864	total: 31.3s	remaining: 22.3s
1941:	learn: 51955.9891914	total: 3m 42s	remaining: 6.66s
190:	learn: 168987.9352649	total: 32.9s	remaining: 18.8s
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1895:	learn: 52545.2618798	total: 3m 36s	remaining: 11.9s
1830:	learn: 53501.7429101	total: 3m 30s	remaining: 19.4s
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1993:	learn: 51968.0886446	total: 3m 48s	remaining: 686ms
195:	learn: 166782.0456323	total: 35s	remaining: 18.6s
[CV 1/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.938, test=0.881) total time= 3.9min
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176:	learn: 178080.2698386	total: 31.6s	remaining: 22s
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1995:	learn: 51960.0285060	total: 3m 48s	remaining: 458ms
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193:	learn: 167851.2236462	total: 33.5s	remaining: 18.3s
177:	learn: 177701.7672853	total: 31.9s	remaining: 21.8s
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196:	learn: 166358.8155203	total: 35.3s	remaining: 18.4s
1834:	learn: 53475.5660763	total: 3m 30s	remaining: 18.9s
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178:	learn: 177300.1735445	total: 32s	remaining: 21.6s
1946:	learn: 51924.6080553	total: 3m 43s	remaining: 6.08s
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1835:	learn: 53470.0035335	total: 3m 30s	remaining: 18.8s
1947:	learn: 51920.5687048	total: 3m 43s	remaining: 5.97s
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1836:	learn: 53464.6888370	total: 3m 30s	remaining: 18.7s
0:	learn: 367865.4763739	total: 154ms	remaining: 46s
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1838:	learn: 53448.4811545	total: 3m 31s	remaining: 18.5s
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196:	learn: 166715.2287877	total: 34s	remaining: 17.8s
1:	learn: 365633.9969916	total: 343ms	remaining: 51.1s
1839:	learn: 53438.8498067	total: 3m 31s	remaining: 18.4s
181:	learn: 176140.0275718	total: 32.4s	remaining: 21s
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1840:	learn: 53432.0402482	total: 3m 31s	remaining: 18.2s
197:	learn: 166330.3703591	total: 34.1s	remaining: 17.6s
182:	learn: 175752.9148422	total: 32.6s	remaining: 20.8s
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1841:	learn: 53423.0734774	total: 3m 31s	remaining: 18.1s
200:	learn: 164920.6203991	total: 36s	remaining: 17.7s
2:	learn: 363476.9557384	total: 585ms	remaining: 57.9s
1903:	learn: 52498.0246718	total: 3m 37s	remaining: 11s
198:	learn: 165991.9116187	total: 34.3s	remaining: 17.4s
1842:	learn: 53420.4384514	total: 3m 31s	remaining: 18s
183:	learn: 175372.1880625	total: 32.7s	remaining: 20.6s
1953:	learn: 51874.7595732	total: 3m 44s	remaining: 5.28s
1904:	learn: 52494.6537781	total: 3m 37s	remaining: 10.9s
3:	learn: 361408.6603351	total: 735ms	remaining: 54.4s
201:	learn: 164563.5426541	total: 36.2s	remaining: 17.5s
199:	learn: 165647.7107330	total: 34.5s	remaining: 17.2s
1954:	learn: 51870.6632994	total: 3m 44s	remaining: 5.16s
184:	learn: 174957.6325094	total: 32.8s	remaining: 20.4s
1843:	learn: 53416.3390172	total: 3m 31s	remaining: 17.9s
1905:	learn: 52477.9225908	total: 3m 38s	remaining: 10.8s
4:	learn: 359320.1060956	total: 879ms	remaining: 51.9s
1844:	learn: 53405.7723955	total: 3m 31s	remaining: 17.8s
1955:	learn: 51863.2161428	total: 3m 44s	remaining: 5.05s
1906:	learn: 52474.1499587	total: 3m 38s	remaining: 10.6s
202:	learn: 164243.8280164	total: 36.3s	remaining: 17.4s
185:	learn: 174579.5297715	total: 33s	remaining: 20.2s
200:	learn: 165266.8925527	total: 34.6s	remaining: 17s
1956:	learn: 51858.0516768	total: 3m 44s	remaining: 4.93s
1845:	learn: 53401.3126167	total: 3m 31s	remaining: 17.7s
5:	learn: 357180.7530212	total: 1.03s	remaining: 50.4s
[CV 2/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.938, test=0.897) total time= 3.8min
1957:	learn: 51852.1207436	total: 3m 44s	remaining: 4.82s
186:	learn: 174152.5156795	total: 33.1s	remaining: 20s
1907:	learn: 52470.5977539	total: 3m 38s	remaining: 10.5s
203:	learn: 163930.3740502	total: 36.5s	remaining: 17.2s
201:	learn: 164918.2587132	total: 34.8s	remaining: 16.9s
1846:	learn: 53393.7243143	total: 3m 31s	remaining: 17.5s
1958:	learn: 51843.3247292	total: 3m 44s	remaining: 4.7s
6:	learn: 355162.6373583	total: 1.17s	remaining: 49s
1908:	learn: 52461.9252003	total: 3m 38s	remaining: 10.4s
187:	learn: 173747.5130386	total: 33.2s	remaining: 19.8s
1847:	learn: 53386.9836155	total: 3m 31s	remaining: 17.4s
204:	learn: 163570.3903213	total: 36.7s	remaining: 17s
1959:	learn: 51838.1445392	total: 3m 44s	remaining: 4.59s
1909:	learn: 52452.2169063	total: 3m 38s	remaining: 10.3s
202:	learn: 164568.2506849	total: 35s	remaining: 16.7s
7:	learn: 353317.8577822	total: 1.34s	remaining: 49s
188:	learn: 173360.8458234	total: 33.4s	remaining: 19.6s
1848:	learn: 53377.8633240	total: 3m 32s	remaining: 17.3s
1910:	learn: 52448.4637474	total: 3m 38s	remaining: 10.2s
1960:	learn: 51829.7025035	total: 3m 44s	remaining: 4.47s
205:	learn: 163212.9117799	total: 36.8s	remaining: 16.8s
0:	learn: 373591.4308469	total: 159ms	remaining: 47.5s
1911:	learn: 52435.0624810	total: 3m 38s	remaining: 10.1s
203:	learn: 164260.1261488	total: 35.2s	remaining: 16.6s
189:	learn: 172979.0152767	total: 33.6s	remaining: 19.4s
1961:	learn: 51825.0397264	total: 3m 45s	remaining: 4.36s
1849:	learn: 53371.7658126	total: 3m 32s	remaining: 17.2s
8:	learn: 351372.3194250	total: 1.58s	remaining: 51.1s
1912:	learn: 52426.7988576	total: 3m 38s	remaining: 9.95s
1:	learn: 371268.6051756	total: 310ms	remaining: 46.2s
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.936, test=0.902) total time= 3.9min
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[CV 4/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.935, test=0.910) total time= 3.9min
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[CV 2/5] END model__bagging_temperature=0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.854, test=0.790) total time=  52.9s
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[CV 1/5] END model__bagging_temperature=0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.856, test=0.785) total time=  55.0s
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[CV 5/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=2000, model__l2_leaf_reg=7, model__learning_rate=0.01, model__loss_function=MAE;, score=(train=0.938, test=0.903) total time= 3.9min
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[CV 3/5] END model__bagging_temperature=0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.845, test=0.822) total time=  53.3s
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[CV 4/5] END model__bagging_temperature=0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.846, test=0.840) total time=  50.0s
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[CV 5/5] END model__bagging_temperature=0, model__depth=10, model__iterations=300, model__l2_leaf_reg=9, model__learning_rate=0.01, model__loss_function=RMSE;, score=(train=0.855, test=0.841) total time=  49.4s
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[CV 1/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.864, test=0.802) total time= 1.3min
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[CV 2/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.852, test=0.831) total time= 1.2min
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[CV 3/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.854, test=0.829) total time= 1.1min
[CV 4/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.846, test=0.861) total time= 1.1min
[CV 5/5] END model__bagging_temperature=1.0, model__depth=8, model__iterations=1000, model__l2_leaf_reg=1, model__learning_rate=0.005, model__loss_function=MAE;, score=(train=0.851, test=0.863) total time= 1.1min
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Best parameters found:  {'model__loss_function': 'RMSE', 'model__learning_rate': 0.05, 'model__l2_leaf_reg': 5, 'model__iterations': 2000, 'model__depth': 4, 'model__bagging_temperature': 0.5}
Best CV score R2:  0.904685750355769
0.9495683457031244
0.8843885999112231